german-sentiment | A data set and model for german sentiment classification | Natural Language Processing library

by oliverguhr Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | german-sentiment Summary

german-sentiment is a Python library typically used in Manufacturing, Utilities, Machinery, Process, Artificial Intelligence, Natural Language Processing, Tensorflow, Bert applications. german-sentiment has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This repository contains the code and data for the Paper "Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems" published at LREC 2020.

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german-sentiment has a low active ecosystem.

It has 45 star(s) with 8 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 9 have been closed. On average issues are closed in 5 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of german-sentiment is current.

Quality

german-sentiment has no bugs reported.

Security

german-sentiment has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

german-sentiment is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

german-sentiment releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed german-sentiment and discovered the below as its top functions. This is intended to give you an instant insight into german-sentiment implemented functionality, and help decide if they suit your requirements.

Run tests
Saves train validation data
Return the config field
Create a file name for a given name
This function cleans up the data from the data loader
Clean text
Replace numbers in text
Split train and validation
Train the model
Print the results
Load valid data
Loads train examples from the given directory
Get train examples from data directory
Load years from a file
Returns a list of train examples
Get train examples from the data directory
Generate dev examples
Get dev examples
LoadsSmarty data from file
Returns a list of test examples
Get dev_matched tsv file
Get config field
Load the sentiment lexicon file
Load SB10k file
Load an IMDB file
Saves test and test validation
Wrapper function for warmup

Get all kandi verified functions for this library.

german-sentiment Key Features

No Key Features are available at this moment for german-sentiment.

german-sentiment Examples and Code Snippets

No Code Snippets are available at this moment for german-sentiment.

Community Discussions

Trending Discussions on german-sentiment

Force BERT transformer to use CUDA

QUESTION

Force BERT transformer to use CUDA

Asked 2021-Jun-13 at 09:57

I want to force the Huggingface transformer (BERT) to make use of CUDA. nvidia-smi showed that all my CPU cores were maxed out during the code execution, but my GPU was at 0% utilization. Unfortunately, I'm new to the Hugginface library as well as PyTorch and don't know where to place the CUDA attributes device = cuda:0 or .to(cuda:0).

The code below is basically a customized part from german sentiment BERT working example

...

ANSWER

Answered 2021-Jun-12 at 16:19

You can make the entire class inherit torch.nn.Module like so:

Source https://stackoverflow.com/questions/67948945

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install german-sentiment

We recommend to install this project in a python virtual environment. To install and activate this virtual environment you need to execute this three commands. Make sure that you are using a recent python version by running "python -V ". You should at least run Python 3.6. Next, install the needed python packages.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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